Instructions to use Tuwhy/Qwen3-4B-OPSA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Tuwhy/Qwen3-4B-OPSA with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Tuwhy/Qwen3-4B-OPSA")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Tuwhy/Qwen3-4B-OPSA")
model = AutoModelForCausalLM.from_pretrained("Tuwhy/Qwen3-4B-OPSA", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Tuwhy/Qwen3-4B-OPSA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Tuwhy/Qwen3-4B-OPSA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tuwhy/Qwen3-4B-OPSA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Tuwhy/Qwen3-4B-OPSA

SGLang

How to use Tuwhy/Qwen3-4B-OPSA with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Tuwhy/Qwen3-4B-OPSA" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tuwhy/Qwen3-4B-OPSA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Tuwhy/Qwen3-4B-OPSA" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tuwhy/Qwen3-4B-OPSA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Tuwhy/Qwen3-4B-OPSA with Docker Model Runner:
```
docker model run hf.co/Tuwhy/Qwen3-4B-OPSA
```

Tuwhy commited on Sep 4

Commit

77b85c6

verified ·

1 Parent(s): 741e410

Add OPSA BibTeX citation

Browse files

Files changed (1) hide show

README.md +14 -1

README.md CHANGED Viewed

@@ -386,7 +386,20 @@ To achieve optimal performance, we recommend the following settings:
 ### Citation
-If you find our work helpful, feel free to give us a cite.
 ```
 @misc{qwen3technicalreport,

 ### Citation
+#### OPSA
+If you find this work useful, please cite:
+```bibtex
+@article{ding2026does,
+  title={Does On-Policy Distillation Really Distill? From Noisy Teacher to Self-Improvement},
+  author={Ding, Yi and Zhang, Ruqi},
+  journal={arXiv preprint arXiv:2608.31046},
+  year={2026}
+}
+```
+#### Base model
 ```
 @misc{qwen3technicalreport,